Trentino Dataset (RMAWGEN)

Trentino daily climate dataset by RMAWGEN R package (https://CRAN.R-project.org/package=RMAWGEN Cordano and Eccel (2017), E. and E. (2016)) contains all information about meteorological stations and instrumental timeseries in 59 sites located in Trentino (Italy) and its neighborood. In particular, it contains daily precipitantion, maximimum and minimum temperature. Original data are provided by Provincia Autonoma di Trento (https://www.meteotrentino.it/), Fondazione Edmund Mach (https://www.fmach.it), Provincia Autonama di Bolzano/Autome Provinz Bozen (http://www.provincia.bz.it/meteo), ARPA Lombardia (https://www.arpalombardia.it/), ARPA Veneto (https://www.arpa.veneto.it/previsioni/it/html/index.php). Two more stations (WWRLUN and WWTRES) provided by the present project are added.

Available Projections (from CORDEX)

The trentino + 2 more stations dataset station locations are inserted within the grid provided by CORDEX outcome files (https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview).

List of RCM and GCM models

Here is a list of available combinations of Global Climate Models (GCMs) and Regional Climate Models RCMS Provided by EURO-CORDEX project provided by Copenicus Data Storage in collaboration with ECMWF (https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=doc,https://confluence.ecmwf.int/display/CKB/CORDEX%3A+Regional+climate+projections, https://climate.copernicus.eu/sites/default/files/2021-03/C3SWebinar1_ErikKjellstrom.pdf )(e.g. Coppola et al. (2021), Vautard et al. (2021), Christensen and Kjellström (2020) https://link.springer.com/article/10.1007/s00382-020-05229-y,).

GCMs:

RCMS:

  • CLMcom-CCLM4-8-17
  • GERICS-REMO2015
  • CLMcom-ETH-COSMO-crCLIM-v1-1
  • DMI-HIRHAM5
  • KNMI-RACMO22E
  • SMHI-RCA4
  • UHOH-WRF361H
  • CNRM-ALADIN63
  • ICTP-RegCM4-6
  • MOHC-HadREM3-GA7-05
  • MPI-CSC-REMO2009

Climate predictions with GCMs are subsequently downscaled at about-10 km resolutions by RCMs.

Monthly Time Series 1951-2005 (historical); 2006-2100 (RCP85)

Averaged daily maximum and minimum temperature

Extracted monthly predicted time series cover the area of grid cells covering in which the station site is contained. Gridded model values from RCMs are subsequently statistical downscaled and then compared with observations, when available, for the historical period (1951-2005) and the RCP8.5 scenario (https://en.wikipedia.org/wiki/Representative_Concentration_Pathway)(2006-2100). Downscaling from the RCM value to the site-scale value has been performed with an ARIMA (p,d,q) model (see arima function in R, base stats package)(Shumway and Stoffer (2017)), in particular the setting (3,1,1) has been selected. The unknowns is the predicted site-scale values whereas the grid-scale prediction from RCM is used as an external regressor. Before calibrated the ARIMA model, the unknown and the external regressor has been Gauassianized (see mormalizeGaussian function in R, RMAWGEN package). Tha ARIMA model has been calibrated for the first 12 years of the time series of observations at WWRLUN and WWTRES. Observation at WWRLUN and WWTRES are available from 2001 to 2022 (March). Downscaled time series with the best fit and tat can be accepted for further analysis are visualized. Scrolling visualization are only available in the HTML of this report.

Godness of fit metrics is based on RMSE (Root Mean Square Error, https://en.wikipedia.org/wiki/Root-mean-square_deviation), MAE (https://en.wikipedia.org/wiki/Mean_absolute_error) and KGE (Kling-Gupta efficiency, see kge function on hydroGOF R package (Mauricio Zambrano-Bigiarini (2020))):

station variable model RMSE MAE KGE
WWRLUN tasmin observation 0.00 0.00 1.00
WWRLUN tasmin MPI-M-MPI-ESM-LR_CLMcom-CCLM4-8-17 2.74 2.11 0.87
WWRLUN tasmin MPI-M-MPI-ESM-LR_CLMcom-ETH-COSMO-crCLIM-v1-1 3.01 2.30 0.79
WWRLUN tasmin MPI-M-MPI-ESM-LR_DMI-HIRHAM5 2.50 1.96 0.89
WWRLUN tasmin MPI-M-MPI-ESM-LR_KNMI-RACMO22E 2.86 2.20 0.85
WWRLUN tasmin MPI-M-MPI-ESM-LR_MOHC-HadREM3-GA7-05 2.53 1.93 0.87
WWRLUN tasmin MPI-M-MPI-ESM-LR_MPI-CSC-REMO2009 4.00 3.05 0.61
WWRLUN tasmin MPI-M-MPI-ESM-LR_SMHI-RCA4 3.04 2.32 0.83
WWRLUN tasmin MPI-M-MPI-ESM-LR_UHOH-WRF361H 7.34 5.94 -0.04
WWRLUN tasmax observation 0.00 0.00 1.00
WWRLUN tasmax MPI-M-MPI-ESM-LR_CLMcom-CCLM4-8-17 2.82 2.25 0.90
WWRLUN tasmax MPI-M-MPI-ESM-LR_CLMcom-ETH-COSMO-crCLIM-v1-1 2.65 2.09 0.92
WWRLUN tasmax MPI-M-MPI-ESM-LR_DMI-HIRHAM5 2.74 2.16 0.89
WWRLUN tasmax MPI-M-MPI-ESM-LR_KNMI-RACMO22E 3.10 2.43 0.86
WWRLUN tasmax MPI-M-MPI-ESM-LR_MOHC-HadREM3-GA7-05 2.76 2.19 0.92
WWRLUN tasmax MPI-M-MPI-ESM-LR_MPI-CSC-REMO2009 3.32 2.61 0.84
WWRLUN tasmax MPI-M-MPI-ESM-LR_SMHI-RCA4 3.40 2.67 0.87
WWRLUN tasmax MPI-M-MPI-ESM-LR_UHOH-WRF361H 3.77 2.96 0.79
WWRLUN tasmin MOHC-HadGEM2-ES_CLMcom-CCLM4-8-17 8.14 6.48 -0.18
WWRLUN tasmin MOHC-HadGEM2-ES_CLMcom-ETH-COSMO-crCLIM-v1-1 8.69 7.03 -0.34
WWRLUN tasmin MOHC-HadGEM2-ES_DMI-HIRHAM5 8.36 6.72 -0.24
WWRLUN tasmin MOHC-HadGEM2-ES_GERICS-REMO2015 8.07 6.44 -0.16
WWRLUN tasmin MOHC-HadGEM2-ES_KNMI-RACMO22E 8.63 6.98 -0.32
WWRLUN tasmin MOHC-HadGEM2-ES_MOHC-HadREM3-GA7-05 6.84 5.46 0.08
WWRLUN tasmin MOHC-HadGEM2-ES_SMHI-RCA4 8.45 6.77 -0.26
WWRLUN tasmin MOHC-HadGEM2-ES_UHOH-WRF361H 8.79 7.11 -0.36
WWRLUN tasmax MOHC-HadGEM2-ES_CLMcom-CCLM4-8-17 9.33 7.62 -0.06
WWRLUN tasmax MOHC-HadGEM2-ES_CLMcom-ETH-COSMO-crCLIM-v1-1 9.94 8.16 -0.81
WWRLUN tasmax MOHC-HadGEM2-ES_DMI-HIRHAM5 9.84 8.05 -0.80
WWRLUN tasmax MOHC-HadGEM2-ES_GERICS-REMO2015 9.14 7.45 -0.02
WWRLUN tasmax MOHC-HadGEM2-ES_KNMI-RACMO22E 10.27 8.46 -0.78
WWRLUN tasmax MOHC-HadGEM2-ES_MOHC-HadREM3-GA7-05 9.10 7.46 0.00
WWRLUN tasmax MOHC-HadGEM2-ES_SMHI-RCA4 9.26 7.50 -0.05
WWRLUN tasmax MOHC-HadGEM2-ES_UHOH-WRF361H 9.85 8.07 -0.79
WWRLUN tasmin MIROC-MIROC5_CLMcom-CCLM4-8-17 3.28 2.50 0.84
WWRLUN tasmin MIROC-MIROC5_GERICS-REMO2015 2.47 1.99 0.89
WWRLUN tasmax MIROC-MIROC5_CLMcom-CCLM4-8-17 3.67 2.83 0.88
WWRLUN tasmax MIROC-MIROC5_GERICS-REMO2015 2.87 2.31 0.92
WWRLUN tasmin ICHEC-EC-EARTH_CLMcom-ETH-COSMO-crCLIM-v1-1 3.08 2.32 0.74
WWRLUN tasmin ICHEC-EC-EARTH_DMI-HIRHAM5 2.80 2.12 0.79
WWRLUN tasmin ICHEC-EC-EARTH_KNMI-RACMO22E 3.00 2.29 0.77
WWRLUN tasmin ICHEC-EC-EARTH_SMHI-RCA4 3.77 2.89 0.63
WWRLUN tasmax ICHEC-EC-EARTH_CLMcom-ETH-COSMO-crCLIM-v1-1 2.83 2.21 0.90
WWRLUN tasmax ICHEC-EC-EARTH_DMI-HIRHAM5 2.62 2.03 0.91
WWRLUN tasmax ICHEC-EC-EARTH_KNMI-RACMO22E 2.94 2.25 0.92
WWRLUN tasmax ICHEC-EC-EARTH_SMHI-RCA4 3.44 2.72 0.86
WWRLUN tasmin CCCma-CanESM2_CLMcom-CCLM4-8-17 2.93 2.32 0.88
WWRLUN tasmin CCCma-CanESM2_GERICS-REMO2015 2.26 1.72 0.93
WWRLUN tasmax CCCma-CanESM2_CLMcom-CCLM4-8-17 3.58 2.73 0.88
WWRLUN tasmax CCCma-CanESM2_GERICS-REMO2015 2.79 2.12 0.92
WWTRES tasmin observation 0.00 0.00 1.00
WWTRES tasmin MPI-M-MPI-ESM-LR_CLMcom-CCLM4-8-17 2.50 1.99 0.82
WWTRES tasmin MPI-M-MPI-ESM-LR_CLMcom-ETH-COSMO-crCLIM-v1-1 2.32 1.82 0.87
WWTRES tasmin MPI-M-MPI-ESM-LR_DMI-HIRHAM5 2.13 1.67 0.91
WWTRES tasmin MPI-M-MPI-ESM-LR_KNMI-RACMO22E 2.58 2.09 0.84
WWTRES tasmin MPI-M-MPI-ESM-LR_MOHC-HadREM3-GA7-05 2.33 1.76 0.85
WWTRES tasmin MPI-M-MPI-ESM-LR_MPI-CSC-REMO2009 2.47 1.97 0.78
WWTRES tasmin MPI-M-MPI-ESM-LR_SMHI-RCA4 2.69 2.21 0.76
WWTRES tasmin MPI-M-MPI-ESM-LR_UHOH-WRF361H 2.82 2.27 0.82
WWTRES tasmax observation 0.00 0.00 1.00
WWTRES tasmax MPI-M-MPI-ESM-LR_CLMcom-CCLM4-8-17 4.11 3.35 0.79
WWTRES tasmax MPI-M-MPI-ESM-LR_CLMcom-ETH-COSMO-crCLIM-v1-1 3.49 2.75 0.84
WWTRES tasmax MPI-M-MPI-ESM-LR_DMI-HIRHAM5 3.24 2.60 0.84
WWTRES tasmax MPI-M-MPI-ESM-LR_KNMI-RACMO22E 4.09 3.27 0.78
WWTRES tasmax MPI-M-MPI-ESM-LR_MOHC-HadREM3-GA7-05 2.71 2.10 0.90
WWTRES tasmax MPI-M-MPI-ESM-LR_MPI-CSC-REMO2009 4.36 3.57 0.76
WWTRES tasmax MPI-M-MPI-ESM-LR_SMHI-RCA4 4.85 4.03 0.73
WWTRES tasmax MPI-M-MPI-ESM-LR_UHOH-WRF361H 4.49 3.64 0.72
WWTRES tasmin MOHC-HadGEM2-ES_CLMcom-CCLM4-8-17 10.89 9.00 -0.80
WWTRES tasmin MOHC-HadGEM2-ES_CLMcom-ETH-COSMO-crCLIM-v1-1 11.38 9.38 -1.40
WWTRES tasmin MOHC-HadGEM2-ES_DMI-HIRHAM5 11.32 9.49 -0.87
WWTRES tasmin MOHC-HadGEM2-ES_GERICS-REMO2015 10.79 8.93 -0.78
WWTRES tasmin MOHC-HadGEM2-ES_KNMI-RACMO22E 11.26 9.29 -0.88
WWTRES tasmin MOHC-HadGEM2-ES_MOHC-HadREM3-GA7-05 11.08 9.33 -0.82
WWTRES tasmin MOHC-HadGEM2-ES_SMHI-RCA4 10.67 8.81 -0.76
WWTRES tasmin MOHC-HadGEM2-ES_UHOH-WRF361H 11.30 9.32 NA
WWTRES tasmax MOHC-HadGEM2-ES_CLMcom-CCLM4-8-17 14.97 12.93 -0.26
WWTRES tasmax MOHC-HadGEM2-ES_CLMcom-ETH-COSMO-crCLIM-v1-1 15.06 12.98 -0.28
WWTRES tasmax MOHC-HadGEM2-ES_DMI-HIRHAM5 14.88 12.83 -0.24
WWTRES tasmax MOHC-HadGEM2-ES_GERICS-REMO2015 14.89 12.85 -0.24
WWTRES tasmax MOHC-HadGEM2-ES_KNMI-RACMO22E 15.31 13.20 -0.32
WWTRES tasmax MOHC-HadGEM2-ES_MOHC-HadREM3-GA7-05 14.72 12.75 -0.19
WWTRES tasmax MOHC-HadGEM2-ES_SMHI-RCA4 15.04 12.99 -0.28
WWTRES tasmax MOHC-HadGEM2-ES_UHOH-WRF361H 15.29 13.19 -0.31
WWTRES tasmin MIROC-MIROC5_CLMcom-CCLM4-8-17 3.53 2.72 0.72
WWTRES tasmin MIROC-MIROC5_GERICS-REMO2015 2.41 1.88 0.91
WWTRES tasmax MIROC-MIROC5_CLMcom-CCLM4-8-17 3.68 2.86 0.87
WWTRES tasmax MIROC-MIROC5_GERICS-REMO2015 3.94 3.21 0.82
WWTRES tasmin ICHEC-EC-EARTH_CLMcom-ETH-COSMO-crCLIM-v1-1 2.15 1.67 0.91
WWTRES tasmin ICHEC-EC-EARTH_DMI-HIRHAM5 2.33 1.77 0.82
WWTRES tasmin ICHEC-EC-EARTH_KNMI-RACMO22E 2.42 1.83 0.86
WWTRES tasmin ICHEC-EC-EARTH_SMHI-RCA4 2.61 2.12 0.76
WWTRES tasmax ICHEC-EC-EARTH_CLMcom-ETH-COSMO-crCLIM-v1-1 3.26 2.60 0.86
WWTRES tasmax ICHEC-EC-EARTH_DMI-HIRHAM5 2.58 2.04 0.91
WWTRES tasmax ICHEC-EC-EARTH_KNMI-RACMO22E 3.22 2.52 0.88
WWTRES tasmax ICHEC-EC-EARTH_SMHI-RCA4 5.03 4.20 0.73
WWTRES tasmin CCCma-CanESM2_CLMcom-CCLM4-8-17 2.84 2.20 0.89
WWTRES tasmin CCCma-CanESM2_GERICS-REMO2015 2.18 1.63 0.91
WWTRES tasmax CCCma-CanESM2_CLMcom-CCLM4-8-17 3.42 2.64 0.88
WWTRES tasmax CCCma-CanESM2_GERICS-REMO2015 3.48 2.74 0.85

Considering only the model combinations with RMSE lower or equal than 4 degrees Celsius and KGE greater than 0.8:

station variable model RMSE MAE KGE
WWRLUN tasmin observation 0.00 0.00 1.00
WWRLUN tasmin MPI-M-MPI-ESM-LR_DMI-HIRHAM5 2.50 1.96 0.89
WWRLUN tasmin MPI-M-MPI-ESM-LR_MOHC-HadREM3-GA7-05 2.53 1.93 0.87
WWRLUN tasmax observation 0.00 0.00 1.00
WWRLUN tasmax MPI-M-MPI-ESM-LR_DMI-HIRHAM5 2.74 2.16 0.89
WWRLUN tasmax MPI-M-MPI-ESM-LR_MOHC-HadREM3-GA7-05 2.76 2.19 0.92
WWRLUN tasmin MIROC-MIROC5_GERICS-REMO2015 2.47 1.99 0.89
WWRLUN tasmax MIROC-MIROC5_GERICS-REMO2015 2.87 2.31 0.92
WWRLUN tasmin CCCma-CanESM2_CLMcom-CCLM4-8-17 2.93 2.32 0.88
WWRLUN tasmin CCCma-CanESM2_GERICS-REMO2015 2.26 1.72 0.93
WWRLUN tasmax CCCma-CanESM2_CLMcom-CCLM4-8-17 3.58 2.73 0.88
WWRLUN tasmax CCCma-CanESM2_GERICS-REMO2015 2.79 2.12 0.92
WWTRES tasmin observation 0.00 0.00 1.00
WWTRES tasmin MPI-M-MPI-ESM-LR_DMI-HIRHAM5 2.13 1.67 0.91
WWTRES tasmin MPI-M-MPI-ESM-LR_MOHC-HadREM3-GA7-05 2.33 1.76 0.85
WWTRES tasmax observation 0.00 0.00 1.00
WWTRES tasmax MPI-M-MPI-ESM-LR_DMI-HIRHAM5 3.24 2.60 0.84
WWTRES tasmax MPI-M-MPI-ESM-LR_MOHC-HadREM3-GA7-05 2.71 2.10 0.90
WWTRES tasmin MIROC-MIROC5_GERICS-REMO2015 2.41 1.88 0.91
WWTRES tasmax MIROC-MIROC5_GERICS-REMO2015 3.94 3.21 0.82
WWTRES tasmin CCCma-CanESM2_CLMcom-CCLM4-8-17 2.84 2.20 0.89
WWTRES tasmin CCCma-CanESM2_GERICS-REMO2015 2.18 1.63 0.91
WWTRES tasmax CCCma-CanESM2_CLMcom-CCLM4-8-17 3.42 2.64 0.88
WWTRES tasmax CCCma-CanESM2_GERICS-REMO2015 3.48 2.74 0.85

Averaged daily precipitation

Whereas temperature is increasing with a significant trend. Nevertheless,the behavior of precipitation is intermittent and discontinous , than it is more difficult to analyze and predict. Some recents studies applied to the Mediterranean area shows a slightly decrease (e.g. Mascaro, Viola, and Deidda (2018)) with an impact of water resources, especially snow (e.g. Senatore et al. (2022)). As concern the site WWTRES and WWRLUN monthly time series is presented in the following. Time series were visualized in the HTML version of this report. Actually model simulated monthly averaged daily precipitation differs from the observed values and are not suitable to downscale with a auto-regressive model like ARIMA. Anyway, in terms of monthly average, precipitation value would vary in the future with a low or insignificant trend. Further studies are difficult to find in literature and required more detailed analysis. Moreover, even if monthly avarage precipitation tends to maintain around values similar to the current and historical ones, precipitation distribution (high intensity event, dry spell duration) over time will significantly change in the future as the global air temperature increases.

References

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